CFTResNet: A novel cross-domain diagnosis framework guided by interpretability for cardiovascular diseases.

Abstract

cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. While deep learning (DL) has shown potential in automated CVDs diagnosis, domain shifts due to variations in acquisition devices and environments hinder generalization and reliability. This paper proposes an interpretable cross-domain diagnostic framework, named CFTResNet, to mitigate domain shifts and enhance diagnostic interpretability. In contrast to traditional transfer learning methods that typically fine-tune fully connected layers (FC), the proposed CFTResNet uses a strategy called Module Robustness Criticality (MRC) to evaluate which parts of the pre-trained model are weak in robustness and then fine-tunes only those specific weak modules instead of adjusting the entire model, thus enhancing adaptability and interpretability. Additionally, to enhance feature representation, we integrate a Temporal-Channel Fusion Module (TCFM) with the ResNet architecture, which effectively captures characteristic information of different channels from heart sound (HS) signals, enhancing the model's capability to discern subtle pathological patterns in cardiac auscultation. Experiments on two public HS datasets demonstrate that CFTResNet outperforms conventional methods in diagnostic accuracy, interpretability, and cross-domain generalization. Highlighting its potential as a reliable AI-assisted (artificial intelligence assisted) tool for clinical CVDs diagnosis.